Well, no one can understand it in a 6 min. video. It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros public class BigONotation {
/** * Big O Notation (how code slows as data grows): * it describes the performance of an algorithm as the amount of data * increases. * * it is machine independent but we are focusing on the "number of steps" to * complete an algorithm. * * examples of Big O notations: * O(1) * O(n) (n = amount of data) * O(log n) * O(n^2) * ... */
/** * concrete example: * addUp1() method will add up to a certain number (n). * * ex: * if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6. * here, the number of steps is 4 because we have one operation * (sum + i) repeated 4 times (n
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
Yoooo, My favorite comp sci. channel is back at it again Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements Keep up the good work broman 😂
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
bro i was a hater for learning bigO notation before watching your video. 😡 cause i cant understand that much.😬 you made me understand this bro. 😘 have you uploaded the "travelling salesman problem" video?🤨
im learning this thing but i have no idea about anything in CS my major doesnt have CS - what should i study before this so i get a basic understanding?
Good thing our professor needed 5 hours to explain that graph...
College is a scam but unfortunately we gotta do it lmfao
Mine explained it in 5 minutes so no one understood it (lol)
At least he came to a conclusion at the end
Cold, crushing grip of academia got you too?
Well, no one can understand it in a 6 min. video.
It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Here too, lol. I didn't understand a single thing, and nobody else did either @@noamrtdthesorcerer733
Those 6 minutes were more useful than 6 months of lectures. Thanks
It's preposterous that you can make everything this simple and smoothly learnable. Thx a lot for real
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
I kinda somewhat get Big O notation now on a high level. that graph helped so much. Google in 3 years here I come!
The guy needs to be seriously appreciated!
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros
public class BigONotation {
/**
* Big O Notation (how code slows as data grows):
* it describes the performance of an algorithm as the amount of data
* increases.
*
* it is machine independent but we are focusing on the "number of steps" to
* complete an algorithm.
*
* examples of Big O notations:
* O(1)
* O(n) (n = amount of data)
* O(log n)
* O(n^2)
* ...
*/
/**
* concrete example:
* addUp1() method will add up to a certain number (n).
*
* ex:
* if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6.
* here, the number of steps is 4 because we have one operation
* (sum + i) repeated 4 times (n
Thank you bro !!!!
Thank you so much Bro
Thank you bro! I am in love with you for this
such a goat fr bro
Tried it, addUp1 is faster compare to addUp2.
addUp2 is only fast if there are more numbers/steps whilst
addUp1 is fast if it is less numbers/steps
That was just an amazing video. Keep up the hardwork and effort you put into your videos.
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
Please keep making more videos about this it helps for interviews thanks bro
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
bro is on the way to 100k 🥳
really looking forward for future vids
The easiness of this man's explanation is incredible
Yoooo, My favorite comp sci. channel is back at it again
Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
Thank you so much bro code, I'm watching your channel,it will grow bigger then your expected
this man is the plug!
I just discovered this channel and goes through the python course I must say...... U deserve 🙏🙏🙏🙏🙏
"Prays" lmao
Thank you! Great code examples to demonstrate the "steps" it takes. :D
You are amazing, Bro!!
Honestly, the best explanation of Big O, thanks you!
You are such a great man keep it going 💞🔥
Super clear and concise. Thanks bro!🎉
Wow. Thanks for helping me understand Big O here than the 3 weeks we spent on in class lol
Universities are about to go bye-bye
This right here is a great man
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements
Keep up the good work broman 😂
this is so easy to understand. thanks bro!
Thank you! This is a great foundation for me to learn more.
Amazing, thank you, bro!
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
I have always enjoyed your humour, cheers and great vid
what humor?
Great explanations! Thanks for share.
fire explanation! thanks!
i love this guy i stg
Underrated!
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
Awesome and simple, thanks a n!
Excellent amazing video. Thumbs up 👍 .
Thanks for these videos man
Many thanks! This video is really good for beginners!
Bro code is different than other tutors xD. awesome
this video explain well the topic. Thank you alot for your time for making this tutorial video.
You always rock it down bro!....huge admiration to yuh !
Nice explanation as usually 👍 🌸
Love your videos, brooo
awesome explanation! Thanks
Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2
This was great!
Thanks a lot for sharing all of this.
wow very good explaination thank you!
Awesome bro
Awesome overview
Love you bro
You're the bro
PLZ MORE DSA. luv u
Respect bro 👊
Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥
Love❤️
Great video!
Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado
After like ten videos, this is the best video by far. 0(1) for sure
Nice explanation Bro!!!
thanks bro!
Thanks my Bro!
Thanks for your efforts
thanks for the short explanation
Thanks!
Legend
You are the best
Great thanks!
Needed this video bro
thanks habibi
Like I always say, my python hero
amaaazing
Lets go!
cool!
Revision covered, my g
Asante kwa maelekozo mazuri
Nice class
so Good thanks Bro Code
You could add the precise definition of Big O notation, not only the intuition behind it
Nice and succinct
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
bro i was a hater for learning bigO
notation before watching your video. 😡
cause i cant understand that much.😬
you made me understand this bro. 😘
have you uploaded the "travelling salesman problem" video?🤨
Thank you Bro
such a amazing explaination by the help of graph 🤩
can you do a tutorial on webpack 5 ?
class video
Thx Bro!!!
ty
this is huuge, well done man.
Thanks bro
Bro can you make tutorial for mips assembly?
which programming language are you gonna use for this DSA course?
Thx bro
thx
im learning this thing but i have no idea about anything in CS
my major doesnt have CS - what should i study before this so i get a basic understanding?
nice
n! getting expelled is crazyyyyy, but I agree lol
Thank you bro code
O(n!) is how much I love each one of your videos.
Hope I got that right😂😂 Love your content.